Departamento de Ingeniería Eléctrica

Partial discharge (PD) monitoring is one of the most used tools for diagnosing the condition of electrical equipment and machines that operate normally at high voltage levels. Ideally, PD identification can be easily done if there is a single source acting over the electrical asset during the measurement. However, in industrial environments, it is common to find the presence of multiple sources acting simultaneously, which hinders the identification process, due to sources of greater amplitude hiding the presence of other types of sources of lesser amplitude that could eventually be much more harmful to the insulation system. In this sense, the separation of PD through the use of clustering techniques allows individual source recognition once they have been clearly separated. This article describes the main clustering techniques that have been used over time to separate PD sources and electrical noise. The results obtained by the different authors in the utilization of each technique demonstrates good performance in terms of separation.

Partial discharges (PDs) are one of the most important classes of ageing processes that occur within electrical insulation. PD detection is a standardized technique to qualify the state of the insulation in electric assets such as machines and power cables. Generally, the classical phase-resolved partial discharge (PRPD) patterns are used to perform the identification of the type of PD source when they are related to a specific degradation process and when the electrical noise level is low compared to the magnitudes of the PD signals. However, in practical applications such as measurements carried out in the field or in industrial environments, several PD sources and large noise signals are usually present simultaneously. In this study, three different inductive sensors have been used to evaluate and compare their performance in the detection and separation of multiple PD sources by applying the chromatic technique to each of the measured signals.

The increasing uptake of converter-interfaced generation (CIG) is changing power system dynamics, rendering them extremely dependent on fast and complex control systems. Regularly assessing the stability of these systems across a wide range of operating conditions is thus a critical task for ensuring secure operation. However, the simultaneous simulation of both fast and slow (electromechanical) phenomena, along with an increased number of critical operating conditions, pushes traditional dynamic security assessments (DSA) to their limits. While DSA has served its purpose well, it will not be tenable in future electricity systems with thousands of power electronic devices at different voltage levels on the grid. Therefore, reducing both human and computational efforts required for stability studies is more critical than ever. In response to these challenges, several advanced simulation techniques leveraging artificial intelligence (AI) have been proposed in recent years. AI techniques can handle the increased uncertainty and complexity of power systems by capturing the non-linear relationships between the system’s operational conditions and their stability without solving the set of algebraic-differential equations that model the system. Once these relationships are established, system stability can be promptly and accurately evaluated for a wide range of scenarios. While hundreds of research articles confirm that AI techniques are paving the way for fast stability assessments, many questions and issues must still be addressed, especially regarding the pertinence of studying specific types of stability with the existing AI-based methods and their application in real-world scenarios. In this context, this article presents a comprehensive review of AI-based techniques for stability assessments in power systems. Different AI technical implementations, such as learning algorithms and the generation and treatment of input data, are widely discussed and contextualized. Their practical applications, considering the type of stability, system under study, and type of applications, are also addressed. We review the ongoing research efforts and the AI-based techniques put forward thus far for DSA, contextualizing and interrelating them. We also discuss the advantages, limitations, challenges, and future trends of AI techniques for stability studies.

Pulsed plasma discharges, such as the plasma focus, are a source of pulsed X rays, therefore it is desirable to understand the relationship between this fast transient phenomena and the electrical variables of the discharge. Parameters from the electrical diagnostic signals are typically used to characterize the plasma focus discharge and for the correlations with X rays measurements via scatter plots. To further evaluate relevant information in the electrical signals, besides the characteristic parameters, an implementation of different types of machine learning algorithms, that included deep learning, was performed. A classification of pulses associated with an X rays measurement, in terms of the electrical signals data as input, was carried out. Two approaches were compared: the selection of the characteristic parameters and the use of the entire signals so the algorithms could find additional information for the classification task. The electrical diagnostic signals corresponded to: the voltage at the electrodes of the discharge chamber measured with a resistive voltage divider; time variation of the circuit current measured with a Rogowski coil and an inductive loop sensor; and the electromagnetic burst from the circuit measured with a Vivaldi antenna. The X rays measurement corresponded to the signal obtained from a scintillator-photomultiplier. In terms of the performance of the algorithms models in this classification problem, the results indicated that there is no significative improvements when using the entire signal or the selection of characteristic parameters. The best results were obtained when the following parameters were used: voltage at time of gas breakdown, voltage at time of pinch, current at time of pinch, time derivative of current at time of pinch, time from breakdown to pinch, and the Fast Fourier Transform of the part of the Vivaldi antenna signal related to the pinch event.

Power systems around the globe are undergoing a transformation characterized by a massive deployment of converter-interfaced generation (CIG) to effectively combat climate change. However, achieving a seamless transition from current power systems dominated by synchronous generators (SGs) to future ones with high levels of CIG requires overcoming several technical challenges. From a frequency stability perspective, reduced system inertia increases the frequency nadir after a loss of generation thereby endangering frequency stability. In this context, this paper proposes a novel methodology for determining control requirements to impose on CIG as their penetration in the network increases. Results of a case study based on the Chilean grid projected for the year 2046 show that, if only grid-following converters without frequency control capability are deployed, a maximum CIG penetration level of 75% can be achieved without threatening frequency stability. The Chilean system can reach a 99% CIG penetration, provided that the remaining CIGs are deployed in grid-following with frequency support capability. Finally, we show that if the last SG is replaced with a grid-forming converter, the system can still sustain frequency stability and exhibits a good dynamic performance. These results demonstrate that, at least from a frequency stability viewpoint, achieving a 100% based CIG system is technically possible. The proposed methodology can be used by energy regulators to define the control requirements necessary to impose on CIG for achieving renewable energy targets in a secure way. Although the obtained results are particular for the Chilean system, the proposed methodology can be applied to any power system

One of the main tools for monitoring the condition of high voltage equipment is the measurement of partial discharges (PD). The electromagnetic (EM) radiation originated from this degradation phenomenon can be captured by various types of ultra-high frequency (UHF) antennas carefully designed and optimised for specific frequency bands. However, the presence of environmental noise may limit the use of this technique. Different types of monopole antennas normally used in UHF PD measurement have been evaluated in order to validate the performance of a novel separation technique of EM sources. Accordingly, a new separation technique based on the energy ratio of the captured signals was developed, considering noise interferences. The results revealed that the new technique allows an adequate separation, even when three sources act simultaneously.

Electrical trees are one of the main mechanisms of degradation in solid polymeric insulation leading to the failure of high voltage equipment. They are interconnected networks of hollow tubules typically characterized from two-dimensional (2D) projections of their physical manifestation. It is shown that complete characterization requires a three-dimensional (3D) imaging technique such as X-ray computed tomography (XCT). We present a comprehensive set of parameters, quantitatively characterizing two types of tree topology, conventionally known as bush- and branchtype. Fractal dimensions are determined from 3D models and from 2D projections, and a simple quantitative relationship is established between the two for all but dense bush trees. Parameters such as number of nodes, segment length, tortuosity and branch angle are determined from tree skeletons. The parameters most strongly indicative of the differences in the structure are the number of branches, individual channel size, channel tortuosity, nodes per unit length and fractal dimension. Studying two stages of a bush tree's development showed that channels grew in width, while macroscopic parameters such as the fractal dimension and tortuosity were unchanged. These parameters provide a basis for tree growth models, and can shed light on growth mechanisms.